KAN-Enhanced Transformer for Wind Profile Retrieval From Lidar Spectra

Accurate detection of wind profiles in the troposphere is essential for atmospheric dynamics research and extreme weather forecasting. Coherent Doppler wind lidar (CDWL) is widely regarded as the most suitable technique for high spatial and temporal resolution wind profile detection. However, since coherent detection relies heavily on the concentration of aerosol particles, which cause Mie scattering, the received backscattering lidar signal exhibits significantly low intensity at high altitudes. As a result, conventional methods, such as the spectral centroid estimator, often fail to produce credible and accurate wind retrieval results in these regions. To address this issue, we introduce a deep learning model for lidar wind profile retrieval, which integrates the Transformer architecture with the Kolmogorov–Arnold network (KAN). Our model is trained solely on targets derived from the traditional wind retrieval algorithm and utilizes radiosonde measurements as the ground truth for test results evaluation. Experimental results demonstrate that our model not only extends the maximum vertical wind profile detection range but also produces more accurate results, exhibiting a level of precision that surpasses the labeled targets. This phenomenon, which we refer to as super-accuracy, is explored by investigating the potential underlying factors that contribute to this intriguing occurrence. In addition, we compare the performance of our method with other state-of-the-art (SOTA) models, highlighting its superior effectiveness and capability in high-resolution wind retrieval. In the future, our proposed model could potentially be extended to other remote sensing spectrum signal-processing applications (e.g., microwave Doppler lidar), thereby establishing a benchmark for future research and driving the advancement of deep learning models in this domain.